Multi-Object Tracking by Iteratively Associating Detections with Uniform Appearance for Trawl-Based Fishing Bycatch Monitoring

• URL: http://arxiv.org/abs/2304.04816v1
• Date: Mon, 10 Apr 2023 18:55:10 GMT
• Title: Multi-Object Tracking by Iteratively Associating Detections with Uniform Appearance for Trawl-Based Fishing Bycatch Monitoring
• Authors: Cheng-Yen Yang, Alan Yu Shyang Tan, Melanie J. Underwood, Charlotte Bodie, Zhongyu Jiang, Steve George, Karl Warr, Jenq-Neng Hwang, Emma Jones
• Abstract summary: The aim of in-trawl catch monitoring for use in fishing operations is to detect, track and classify fish targets in real-time from video footage. We propose a novel MOT method, built upon an existing observation-centric tracking algorithm, by adopting a new iterative association step. Our method offers improved performance in tracking targets with uniform appearance and outperforms state-of-the-art techniques on our underwater fish datasets as well as the MOT17 dataset.
• Score: 22.228127377617028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The aim of in-trawl catch monitoring for use in fishing operations is to detect, track and classify fish targets in real-time from video footage. Information gathered could be used to release unwanted bycatch in real-time. However, traditional multi-object tracking (MOT) methods have limitations, as they are developed for tracking vehicles or pedestrians with linear motions and diverse appearances, which are different from the scenarios such as livestock monitoring. Therefore, we propose a novel MOT method, built upon an existing observation-centric tracking algorithm, by adopting a new iterative association step to significantly boost the performance of tracking targets with a uniform appearance. The iterative association module is designed as an extendable component that can be merged into most existing tracking methods. Our method offers improved performance in tracking targets with uniform appearance and outperforms state-of-the-art techniques on our underwater fish datasets as well as the MOT17 dataset, without increasing latency nor sacrificing accuracy as measured by HOTA, MOTA, and IDF1 performance metrics.

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